Overview

The implementation of AI in customer service has massive impact on the work environment of service employees. In the future it is expected that customer service employees will work closely with AI agents forming human-AI teams. Emotions played a significant role in customer service even before the introduction of AI. It can be assumed that, that implementing AI agents will impact employees' emotions during customer interaction in new ways and evolve over time. 

AI can not only help in supporting answering customer inquiries, but also potentially provide support to customer services employees in managing their emotions. However, it is unclear what requirements exist for appropriate emotional support and what design principles should be followed in its implementation. Therefore, the following question needs to be addressed as part of the work:

What requirements and principles should guide personalized emotion support for service employees in AI-augmented customer service?

Goal of the Thesis: 

The thesis focuses on designing personalized emotional support in AI-augmented customer service. The student will:  

• Analyze the current literature on AI-augmented customer service and the role of emotions
• Interview customer service employees before and after AI implementation
• Articulate design principles, suggest interventions, and design prototypes for personalized emotional support

The thesis is carried out in collaboration with Allianz Versicherung AG.

Recommended skills:

• Basic knowledge in Python
• Interest in Human-Computer Interaction/Human-AI Collaboration
• Interest in quantitative research
• Strong time management and communication skills, and proficiency in English

References

• Fortuna, A.; Prasetya, F.; Samala, D.; Rawas, S.; Kaya, D. Artificial intelligence in personalized learning: A global systematic review of current advancements and shaping future opportunities, Social Sciences & Humanities. (2025).
• Huang, M.-H., & Rust, R. T. The Caring Machine: Feeling AI for Customer Care. Journal of Marketing, 88 (5), (2024). 1-23.